Carbon footprint accounting method and system for styrene product based on list data optimization

Abstract

The invention discloses a carbon footprint accounting method and system for styrene products based on list data optimization. The problems that existing carbon footprint accounting is relatively lagged and inaccurate are solved, and the method comprises the steps that modular deconstruction is conducted on a technological process, an emission list is constructed, and data are collected; distributing module product carbon footprints according to mass, and constructing a module accumulation carbon footprint model; calculating module accumulated carbon footprints by combining all module accumulated carbon footprint models; and calculating a material sensitivity coefficient and a quality evaluation coefficient to construct a four-quadrant graph, and optimizing material data. According to the method, the rationality of the list data is evaluated and optimized, the data quality guarantee in the carbon footprint calculation process is enhanced, and the carbon footprint calculation result is more accurate. The complex flow relation of materials and energy among all production units is considered, and the problems that in the prior art, comprehensive and accurate actual production data is difficult to obtain, background database support is insufficient, and consequently an accounting result cannot accurately reflect the current product carbon footprint are solved.

Description

Technical Field

[0001] This invention relates to the field of carbon footprint accounting technology, and in particular to a method and system for carbon footprint accounting of styrene products with optimized inventory data. Background Technology

[0002] Styrene, a typical petrochemical intermediate, is widely used in the production of downstream materials such as polystyrene, ABS resin, and synthetic rubber. Its life cycle encompasses multiple raw material routes (such as the ethylbenzene process and the benzene-propylene co-reaction process), complex processes (such as pyrolysis, dehydrogenation, separation, and purification), diverse byproducts (such as ethylbenzene, benzene, propylene, and methane), and a long supply chain, involving high-carbon emission stages such as high-temperature, catalytic reactions, and multi-energy coupling. Therefore, a systematic and comparable carbon footprint accounting method is of great significance for the green design of styrene products and emission reduction in the industrial chain.

[0003] However, research on carbon footprint accounting for petrochemical intermediates such as styrene is still relatively lagging behind, with the following main problems: (1) Significant differences in process paths and carbon footprints: Different companies use different styrene production processes, with different equipment boundaries, raw material composition, energy consumption levels and by-product flows. If a uniform template is used for accounting, it is difficult to truly reflect the different carbon emission characteristics of companies.

[0004] (2) Diverse products and complex reflux paths make it difficult to accurately calculate the carbon emissions of intermediate products: The styrene process involves various intermediate products and reflux modules. These intermediate links not only serve the target product but may also be used as raw materials for other product paths. Existing accounting methods mostly focus on the final product and cannot take into account the allocation of intermediate products and their carbon footprint in the multi-path system, resulting in insufficient applicability and comparability of the accounting results. For example, patent number 201611019876.6, entitled "A Method for Measuring the Carbon Footprint of Petrochemical Products," uses a life-cycle approach to calculate the carbon footprint of petrochemical products. It mainly calculates carbon emissions based on crude oil extraction, crude oil transportation, petrochemical product emissions, product sales, and petrochemical product use and disposal. This patent does not consider the cross-circulation of materials between units for calculating the carbon emissions of petrochemical product units, leading to inaccurate carbon footprint calculations in the production process.

[0005] (3) Disconnect between emission data and production system structure: Existing carbon footprint methods mostly present emission data in the form of lists, lacking a system mapping with process flow or module structure, making it difficult to reflect the correspondence between each emission source and specific modules, and between material and energy flow paths. This fragmentation makes it difficult to support the comprehensive identification and accurate classification of carbon emissions in complex systems without unified rules for emission inventory compilation. Summary of the Invention

[0006] This invention primarily addresses the problems of existing carbon footprint accounting methods, such as the inability to reflect the differences in carbon emissions among enterprises using a uniform template, the difficulty in considering the allocation of intermediate products and their carbon footprint attribution within the system when focusing on final products, and the lack of mapping between data presented as a list and modular systems. It provides a carbon footprint accounting method and system for styrene products with optimized list data.

[0007] The above-mentioned technical problem of the present invention is mainly solved by the following technical solution: a carbon footprint accounting method for styrene products with optimized inventory data, comprising the following steps: Modular deconstruction of the target product's process flow; Constructing an emissions inventory and collecting emissions inventory data; The carbon footprint of the module products is allocated by quality, and the cumulative carbon footprint of intermediate products is added to construct the module's cumulative carbon footprint model. Calculate the cumulative carbon footprint of the module by combining the cumulative carbon footprint models of all modules; The system acquires characteristic values ​​from the module emission inventory data that affect the changes in the product's carbon footprint, calculates the material sensitivity coefficient, sets material quality assessment indicators to calculate the material quality assessment coefficient, constructs a four-quadrant diagram based on the material sensitivity coefficient and the quality assessment coefficient, classifies materials into the four-quadrant diagram to determine material data assessment, and optimizes the material data based on the assessment.

[0008] This invention combines sensitivity coefficients and quality assessment coefficients to evaluate and optimize the rationality of inventory data, strengthening data quality assurance during carbon footprint calculation and making the calculation results more accurate, scientifically transparent, and highly reliable and comparable. This invention modularly deconstructs the target product's process flow, constructing a carbon footprint model based on the numerous material recirculations and recycling involved in each module. The combined modular carbon footprint models calculate the carbon footprint of each module, considering the complex flow relationships of materials and energy between production units within the production process. It also addresses the problem of existing methods struggling to obtain comprehensive and accurate actual production data and insufficient background database support, which leads to inaccurate calculation results reflecting the current product's carbon footprint. This invention uses a modular matrix modeling method to refine and quantify carbon emission sources throughout the target product's entire lifecycle in a modular manner, ensuring high accuracy and comparability in carbon footprint calculation. This invention achieves precise control of carbon footprint calculation by clarifying the emission sources of different production stages and intermediate products through the carbon footprint of each module, reducing the ambiguity or omissions in lifecycle boundaries found in existing methods and ensuring comprehensive coverage of carbon emission sources. This study optimizes the methods for collecting and processing carbon footprint data. Through modularization and manifold integration, it systematically establishes the relationship between emission sources and emission categories, improving the efficiency and accuracy of data collection and providing reliable data support for subsequent carbon footprint analysis. It also enhances the accuracy of carbon footprint accounting for intermediate products and recycle processes, overcoming the insufficient attention paid to these areas in existing methods and ensuring that all carbon emission pathways and stages are appropriately included in the carbon footprint accounting system. Furthermore, it strengthens the applicability and flexibility of carbon footprint accounting by leveraging the scalability of modular multivariate equation solving methods. This allows the method to adapt to petrochemical production processes of different scales and processes, demonstrating strong universality and adaptability.

[0009] As a preferred method, calculating the material sensitivity coefficient includes: The feature values ​​are obtained, including material values ​​and corresponding material carbon footprint factors. The carbon footprint of the target product is calculated based on the cumulative carbon footprint of the module. The material sensitivity coefficient is obtained by calculating the product of the material value and the material carbon footprint factor, and then comparing it with the carbon footprint of the target product.

[0010] This scheme performs sensitivity analysis on the values ​​in the styrene lifecycle inventory data to obtain the relative impact of changes in each inventory data point on the carbon footprint calculation results, which is the sensitivity coefficient. A high sensitivity coefficient for a data point indicates a significant impact of changes on the results. The impact of inventory data on carbon footprint results comes from two aspects: changes in the material values ​​themselves, such as material input, transportation distance, and energy consumption; and changes in the corresponding carbon footprint coefficient. Whether changing the material value or the carbon footprint factor, the formula for the sensitivity coefficient's contribution to the total carbon footprint remains the same: Sensitivity Coefficient S. iThe calculation formula is expressed as follows: Among them, A i For the i-th material value, EF i Let be the carbon footprint factor of the i-th material, and PCF be the carbon footprint of the target product of the module.

[0011] As a preferred method, the calculation of material quality assessment coefficients includes: Set the evaluation benchmark and corresponding score value for each quality assessment indicator, and obtain the score value of the quality assessment indicator by comparing the material data of the bill of materials with the evaluation benchmark. The quality assessment coefficient is obtained by averaging the scores of each evaluation indicator.

[0012] A five-dimensional Pedigree matrix scoring method was adopted, and quality assessment indicators were set, including time representativeness, geographical representativeness, technical relevance, data representativeness, and data completeness. Evaluation benchmarks were determined for each quality assessment indicator, and each benchmark was assigned a corresponding score. Material data was compared with the evaluation benchmarks of the quality assessment indicators to obtain the corresponding quality assessment indicator scores. A quality assessment coefficient was calculated by combining the scores of all quality assessment indicators; this quality assessment coefficient is the average of the scores of all evaluation indicators. A higher quality assessment coefficient indicates higher material data quality.

[0013] As a preferred approach, a four-quadrant diagram is constructed based on the material sensitivity coefficient and quality assessment coefficient. Materials are then categorized into the four-quadrant diagram to determine material data assessment, including: The material sensitivity coefficient and quality assessment coefficient were calculated using normalization. A coordinate system is established using the normalized sensitivity coefficient and the normalized quality assessment coefficient. An intermediate value is set to divide the data into four quadrants, and the material data assessment for each quadrant is set. Materials are categorized into a four-quadrant diagram based on normalized sensitivity coefficients and normalized quality assessment coefficients to obtain material data evaluation.

[0014] This solution employs a data quality assessment method, normalizing the material sensitivity coefficient and quality assessment coefficient to establish a two-dimensional coordinate system. The normalized sensitivity coefficient and normalized quality assessment coefficient are used as the vertical and horizontal axes, respectively. An intermediate value for the normalized sensitivity coefficient and normalized quality assessment coefficient is set to divide the coordinate system into a four-quadrant diagram. Material data assessment is defined for each quadrant. Based on the material data assessment, the materials are optimized, and the optimized data is fed back to the cumulative carbon footprint model. The carbon footprint data calculated by the cumulative carbon footprint model is more accurate.

[0015] As a preferred embodiment, the modular deconstruction of the target product's process flow includes: Define the target product's process flow, and divide the process flow into modules, with the production unit where the product flows out and the data is available as the smallest unit, resulting in multiple modules.

[0016] By analyzing the process flow and taking the production unit with output as the smallest unit, the process flow diagram is structurally decomposed and encapsulated, breaking down the styrene system into multiple independent or interconnected functional modules to facilitate analysis, optimization, and calculation. The key to modular deconstruction is to clearly define the module boundaries, ensuring that each module can be calculated independently while also being interconnected within the overall system.

[0017] As a preferred approach, the carbon footprint of module products is allocated by quality, including: Obtain the total mass of all output products of the module, including target products, intermediate products and by-products. Calculate the proportion of each product in the total mass of all products to obtain the mass score of each product. Calculate the carbon footprint of each product based on the module's cumulative carbon footprint and the product's mass score.

[0018] The styrene system contains multiple modules. Module i may use intermediate products produced by any other module, and there is some recirculation between modules (i.e., some outputs re-enter other upstream modules). During model building, the carbon emissions calculation for each module is first based on its inputs (raw materials, energy, etc.) and process emissions to calculate the independent carbon footprint excluding recirculated materials. Then, applying the mass fraction principle, the module's carbon footprint is allocated to each product, forming the product carbon footprint (including the carbon footprint of recirculated materials). Following a hierarchical accounting method, the independent carbon footprints of each module are summed with the carbon footprints carried by all materials recirculated to that module, forming the final cumulative carbon footprint of the module.

[0019] As a preferred option, the cumulative carbon footprint model for building the carbon footprint of intermediate products includes: Establish a module-independent carbon footprint model based on the module's inputs and outputs; Obtain the carbon footprint of the intermediate products from the input module; A modular cumulative carbon footprint model is established by combining the input intermediate product carbon footprint and the independent carbon footprint model.

[0020] First, construct an independent carbon footprint model for each module, excluding intermediate products as materials, to obtain the module's independent carbon footprint. The total carbon footprint of the module, including the carbon footprint of intermediate recycle products as raw materials, is the module's cumulative carbon footprint, and the model constructed for it is the cumulative carbon footprint model.

[0021] Specifically, the module cumulative carbon footprint model is the sum of the intermediate product carbon footprints of all other modules and the independent carbon footprint of the current module. The independent carbon footprint is calculated by the independent carbon footprint module.

[0022] As a preferred approach, the cumulative carbon footprint of the module is calculated by simultaneously establishing the cumulative carbon footprint model of all modules, including: Obtain the cumulative carbon footprint model construction equation set for all modules; Establish a quality allocation matrix A based on the system of equations. The elements of matrix A are the quality fractions of intermediate products from module j used by module i in module j. Establish an independent carbon footprint matrix B, where the elements of matrix B are the independent carbon footprints of each module; Establish a matrix X to be solved, where the elements of matrix X are the cumulative carbon footprints of each module; Construct matrix equations and solve matrix X to obtain the cumulative carbon footprint of each module.

[0023] This scheme constructs a module quality allocation coefficient matrix based on the flow relationship of intermediate products between modules, and solves for the cumulative carbon footprint of each module using linear algebra. First, a system of equations is constructed based on the cumulative carbon footprint model of each module. By constructing a quality allocation matrix A, an independent carbon footprint matrix B, and a matrix to be solved, i.e., the cumulative carbon footprint matrix X, the cumulative carbon footprint of each module satisfies the linear equation system A×X=B. Through matrix operations, using the inverse matrix conventional linear algebra method, matrix X is solved to obtain the cumulative carbon footprint of each module.

[0024] As a preferred approach, a module-independent carbon footprint model is established based on the module's inputs and outputs, including: Determine the scope of accounting for the target product; Based on the module deconstruction results, the flow relationships between modules are clarified, the emission information within each module is integrated, and a module manifold diagram is constructed. Establish an emissions inventory and obtain emissions inventory data; Establish modular independent carbon footprint models based on direct emissions, indirect energy emissions, and emissions from upstream and downstream of the supply chain.

[0025] A carbon footprint accounting system for styrene products with optimized inventory data, comprising a target determination unit that determines the target product and the accounting scope; The module deconstruction unit deconstructs the target product's process flow into multiple modules; The model generation unit establishes a module-independent carbon footprint model and a module-cumulative carbon footprint model based on the input intermediate product carbon footprint and the independent carbon footprint model. The carbon footprint calculation unit calculates the cumulative carbon footprint of the module by combining the cumulative carbon footprint models of all modules. The data optimization unit calculates the material sensitivity coefficient and material quality assessment coefficient, constructs a four-quadrant diagram, classifies materials into the four-quadrant diagram to determine material data assessment, and optimizes the material data based on the assessment.

[0026] Therefore, the advantages of the present invention are: 1. By combining sensitivity coefficient and quality assessment coefficient to evaluate and optimize the inventory data, the data quality assurance in the carbon footprint calculation process is strengthened, making the carbon footprint calculation results more accurate, scientific and transparent, and highly credible and comparable.

[0027] 2. The target product process flow is deconstructed into modules. A carbon footprint model is constructed based on the numerous material recirculation and recycling involved in each module. The carbon footprint models of the modules are combined to calculate the carbon footprint of each module. The complex flow relationship of materials and energy between production units within the production process is taken into account. At the same time, it solves the problem that it is difficult to obtain comprehensive and accurate actual production data and the background database is insufficient, which leads to the calculation results not accurately reflecting the current carbon footprint of the product.

[0028] 3. By using a modular matrix modeling method, the carbon emission sources throughout the entire life cycle of the target product are refined and quantified in a modular manner to ensure that the carbon footprint accounting has high accuracy and comparability.

[0029] 4. Achieve precise control of carbon footprint accounting. By identifying the carbon footprint of each module, clarify the emission sources of different production stages and intermediate products, reduce the situation of unclear life cycle boundaries or omissions in existing methods, and ensure comprehensive coverage of carbon emission sources.

[0030] 5. Optimize the methods for collecting and processing carbon footprint data. By integrating modularization and manifold diagrams, systematize the relationship between emission sources and emission categories, improve the efficiency and accuracy of data collection, and provide reliable data support for subsequent carbon footprint analysis.

[0031] 6. Improve the accuracy of carbon footprint accounting for intermediate products and recycle processes, overcome the problem of insufficient attention to intermediate products and recycle processes in existing methods, and ensure that all carbon emission pathways and links are reasonably included in the carbon footprint accounting system.

[0032] 7. Enhance the applicability and flexibility of carbon footprint accounting. Through the scalability of the modular multivariate equation solution method, the method can be adapted to petrochemical production processes of different scales and processes, and has strong universality and adaptability. Attached Figure Description

[0033] Figure 1 This is a flowchart of the present invention.

[0034] Figure 2 This is a schematic diagram of the module association of styrene in this invention.

[0035] Figure 3 This is a schematic diagram of a modular manifold of styrene according to the present invention.

[0036] Figure 4This is a schematic diagram illustrating the generation of an emission inventory according to the present invention.

[0037] Figure 5 This is a schematic diagram of a four-quadrant diagram of the present invention. Detailed Implementation

[0038] The technical solution of the present invention will be further described in detail below through embodiments and in conjunction with the accompanying drawings.

[0039] Example: This embodiment presents a method for calculating the carbon footprint of styrene products based on inventory data optimization, such as... Figure 1 As shown, it includes the following steps: S1. Process flow analysis By conducting a detailed analysis of the production process, verifying the scope of accounting and the included devices, identifying the material and energy flows of each device, and the direct discharge and disposal measures for the three wastes (waste gas, wastewater, and solid waste), the completeness and scientific nature of the accounting process are ensured.

[0040] Determine the scope of carbon footprint accounting for the target product.

[0041] By analyzing and verifying actual process diagrams (such as process flow diagrams (PFD) or process piping and instrumentation diagrams (PID)), process flow diagrams and pollution generation node diagrams or material balance sheets, the first process equipment, the reformate separator, into which the initial raw material reformate enters is determined as the starting point of the accounting scope; the last process equipment, the condenser, into which the final product styrene leaves the production system is determined as the ending point of the accounting scope.

[0042] Analysis of process flow Material flow analysis: Identify the flow paths of main raw materials and products, determine key feeding points, and the material conversion relationships of each unit.

[0043] Energy flow analysis: Identify the main energy inputs (such as electricity, natural gas, steam, etc.) and the equipment using them in the process.

[0044] Greenhouse gas emissions and waste analysis: Identify the main direct emission points of greenhouse gases (CO2, CH4, N2O, etc.) in the process; identify the sources and treatment methods of wastewater, waste gas, and solid waste.

[0045] S2. Modularly deconstruct the process flow of the target product.

[0046] Define the target product's process flow and divide the process flow into modules, with the production unit from which the product flows out as the smallest unit. This results in multiple modules.

[0047] Using the production unit with product output and available data as the smallest unit, the process flow diagram is structurally decomposed and encapsulated, breaking down the styrene system into multiple independent or interconnected functional modules to facilitate analysis, optimization, and calculation. The key to modular deconstruction lies in clearly defining module boundaries, ensuring that each module can be calculated independently while also being interconnected within the overall system.

[0048] The minimum accounting module should be determined, including the following requirements: Define the production process flow: Based on the process flow analysis, the smallest unit is the production unit with product outflow.

[0049] Identify data availability: Analyze the availability of input, output, and emission data for each process unit.

[0050] Separate modeling: For production units with complete data and independent calculation capabilities, they are directly defined as accounting modules to ensure high resolution in carbon footprint calculation.

[0051] Based on this embodiment, the styrene process flow is divided, and the production units in the styrene process flow are determined, including an aromatics complex, an ethylene unit, an ethylbenzene unit, and a styrene unit. According to the product flow production unit as the smallest unit, the aromatics complex includes multiple production units that produce products respectively. Therefore, the aromatics complex is divided into four modules: aromatics fractionation, extraction, disproportionation and isomerization, and adsorption separation. The ethylene unit is one module, the ethylbenzene unit is one module, and the styrene unit is one module, for a total of six modules.

[0052] Determine the start module and the end module The start module and the end module define the entire accounting boundary, ensuring the integrity of the system's input and output.

[0053] The starting module is the starting point of the accounting model. It is the first step in the production process, directly accepting external raw material input and starting the process transformation, namely the aromatics fractionation module. The final module is the module that produces the final product, namely the styrene unit.

[0054] S3. Based on the flow relationships between modules, establish a module association diagram.

[0055] Based on the modular deconstruction of the process flow, a module correlation diagram is drawn according to the material flow, energy flow, and emission flow relationships between modules. The core purpose of the module correlation diagram is to clearly present the interactions between modules while ensuring the independence of the accounting modules, providing accurate boundary definitions and data flow information for carbon footprint calculation.

[0056] S31. Module Product Flow Analysis Based on the process flow diagram, the product flow of each module is further clarified to ensure that all material flows during styrene production are accurately tracked, providing complete data support for subsequent carbon footprint calculations. The connections between different modules depend on the material flow direction, such as main products, by-products, and recycle materials, helping to establish the input-output relationships of the modules.

[0057] Product flow refers to the flow of products, by-products, and intermediate products. The direction of these flows directly affects the system boundary and allocation principles for carbon footprint calculation. Based on process flow analysis, the input and output materials of each module are identified and their destinations are determined for subsequent module relationship diagram drawing.

[0058] S32. Module Association Diagram Construction Based on product flow analysis, the material flow relationships between modules are determined, identifying the input and output material flows of each module, including main products, by-products, return flows, and waste flows. This ensures that all material flow paths are traced, avoiding the omission of key carbon footprint sources. Specific operations include: Represent each independent module with rectangles; use arrows to indicate the flow of materials, including raw material input, product output, and by-product return; for cyclical flows such as by-product return to preceding modules, ensure clear connections and label names and types on the lines, constructing a module relationship diagram as follows. Figure 2 As shown.

[0059] S3. Based on the module association diagram, integrate the emission information within each module to construct a module manifold diagram.

[0060] Based on the module association diagram, information on Scope 1 emissions (direct emissions), Scope 2 emissions (indirect energy emissions), and Scope 3 emissions (upstream and downstream emissions in the supply chain) within each module is integrated to form a module manifold diagram for accounting. Through systematic emission path analysis, it is ensured that all carbon emission sources are reasonably assigned to the corresponding accounting modules, providing more accurate system boundaries and data inputs for carbon footprint calculation. The input logistics for each module includes raw materials, auxiliary materials, and utility supplies (such as steam and electricity). The output logistics includes main products, by-products, and waste, ensuring that all logistics paths are traceable. Specifically, carbon emission sources are labeled using different symbols or colors to distinguish emission types; emission sources of the same type and their connecting lines use the same color; emission points should correspond to the accounting list data to ensure the accuracy of the calculation.

[0061] Standardized manifold diagram drawing method: Different flow directions are represented using standardized symbols. The constructed styrene manifold diagram is shown below. Figure 3 As shown.

[0062] S4. Establish an emissions inventory and obtain emissions inventory data.

[0063] Establish an emission inventory framework: Based on the connection relationships, classify the emission sources of each module according to the standard structure of "module name - emission category - emission unit name" to form a standardized emission inventory format. Each emission point on each module connection line must have a corresponding entry in the emission inventory table to ensure the integrity and traceability of emission data.

[0064] Data collection and entry: Emissions inventory data collection includes real-world data and background data. Real-world data is collected from enterprise MES, ERP systems, and production reports, extracting consumption, energy consumption, and emissions monitoring data. Background data is collected by filling the emission factor database with environmental impact factors from databases such as ecoinvent and CLCD.

[0065] Following the logic of the module manifold diagram, the corresponding emission data are filled into the emission inventory. The emission sources in the manifold diagram correspond one-to-one with the data in the emission inventory, such as... Figure 4 As shown, ensure consistency between the wiring logic and modules. For unavailable field data, use methods such as engineering estimation or industry averages, and indicate the data quality level and source of uncertainty.

[0066] S5. Construct a module carbon footprint model.

[0067] S51. Assign the carbon footprint of module products by quality to obtain the carbon footprint of intermediate products.

[0068] Obtain the total mass of all output products of the module, including target products, intermediate products and by-products. Calculate the proportion of each product in the total mass of all products to obtain the mass score of each product. Calculate the carbon footprint of each product based on the module's cumulative carbon footprint and the product's mass score.

[0069] The styrene system contains multiple modules. Module i may use intermediate products produced by any other module, and there is some recirculation between modules (i.e., some outputs re-enter other upstream modules). During model construction, the carbon emissions calculation for each module is first based on its inputs (raw materials, energy, etc.) and process emissions to calculate an independent carbon footprint excluding recirculated materials. Then, the mass fraction principle is applied to allocate the module's carbon footprint to each product, forming the product carbon footprint (including the carbon footprint of recirculated materials).

[0070] The products produced by the modules include intermediate products and by-products, with the final module at the very bottom also including the target product.

[0071] Except for the final downstream module, for all other modules, module I ∈ [1, n-1], I ≠ j, the quality scores of k intermediate product IPs produced by module j ∈ [1, n] are: Where n represents the total number of modules, MIP I-j，k M represents the quality of k intermediate products that flow from module I to module j. I surface The sum of the mass of all products in module I, MIP I MBP represents the sum of the masses of all intermediate products produced by module I. I This represents the sum of the masses of all byproducts generated by module I.

[0072] Module I produces k by-products (BP) with varying quality fractions: Among them, mBP I，k This represents the quality of k by-products produced by module I.

[0073] For the final downstream module, the quality scores of all products include, Module n: Production target product quality score Where module n is the downstream module, M n MP represents the sum of the masses of all products in module n. n Indicates the target product quality of module n, MIP n MBP represents the sum of the qualities of all intermediate products produced by module n. n This represents the sum of the qualities of all byproducts generated by module n.

[0074] The quality score of k intermediate products (IP) from module n to module j: Among them, MIP n-j,k M represents the quality of k intermediate products that flow from module n to module j. n This represents the sum of the quality of all products in module n.

[0075] Module n produces k by-products BP quality fractions: Among them, MBP n，k This represents the quality of k by-products produced by module n.

[0076] The carbon footprint of each product in a module is calculated by multiplying the quality score of each product in that module by the cumulative carbon footprint of that module. Based on this, the carbon footprint of the target product can be obtained: PCF = fP i *CF i , i = n.

[0077] S52. Establish a module-independent carbon footprint model based on the module's inputs and outputs.

[0078] The independent footprint model is the sum of carbon emissions from Scope 1, Scope 2, and Scope 3. Therefore, the carbon footprint of module i is represented as follows: ICF i =CFS1 i +CFS2 i +CFS3 i Among them, ICF i Indicates the independent carbon footprint of module i, CFS1 i Indicates the range of carbon emissions for module i, CFS2 i Indicates the range of carbon emissions for module i, CFS3 i This indicates the range of carbon emissions for module i.

[0079] Module i's scope - carbon emissions: CFS1 i =∑S1Ei,k*PCF DEi，k_CO2 Module i's scope of carbon emissions: CFS2 i =∑S2Ei,k*PCF Ei，k_CO2 Module i's scope includes three carbon emissions: CFS3 i =∑S3Mi,k*PCF Mi，k_CO2 +∑S3Ti,k*PCF Ti，k_CO2 +∑S3WTi,k*PCF WTi,k_CO2 Among them, PCF α α represents the carbon footprint factor of any item in the list, and α represents any item in the list of S2Ei, S3Mi, S3Ti, S1DEi, and S3WTi. S2Ei represents purchased thermal power, S3Mi represents purchased raw materials, S3Ti represents the transportation of purchased raw materials, S1DEi represents emissions directly generated at the production site, and S3WTi represents waste that can be discharged after further treatment.

[0080] S53. Combine the input intermediate product carbon footprint and the module-independent carbon footprint model to establish a module-cumulative carbon footprint model.

[0081] The cumulative carbon footprint of a module is calculated using the module cumulative carbon footprint model. The module cumulative carbon footprint, based on the individual carbon footprints, includes the carbon footprint of intermediate recycle products used as raw materials, reflecting the complex material flow relationships between modules. The cumulative carbon footprint model for module i∈[1,n], i≠j (module i includes all modules I and n) and the cumulative carbon footprint of any module j supplying intermediate products is as follows: Among them, CF i fIP represents the cumulative carbon footprint of module i. j-i This indicates that module j produces and supplies module i with k as raw material. The quality score of the intermediate product in module j among all products, CF j This represents the cumulative carbon footprint of module j.

[0082] S6. Simultaneously solve the cumulative carbon footprint models of all modules to calculate the cumulative carbon footprint of the module. Specifically, this includes: Obtain the cumulative carbon footprint model construction equation set for all modules; Establish a quality allocation matrix A based on the system of equations. The elements of matrix A are the quality fractions of intermediate products from module j used by module i in module j. Establish an independent carbon footprint matrix B, where the elements of matrix B are the independent carbon footprints of each module; Establish a matrix X to be solved, where the elements of matrix X are the cumulative carbon footprints of each module; Construct matrix equations and solve matrix X to obtain the cumulative carbon footprint of each module.

[0083] Based on the flow relationships of intermediate products between modules, a module quality allocation coefficient matrix is ​​constructed, and the cumulative carbon footprint of each module is solved using linear algebra methods. The seven modules in this embodiment are denoted as Module 1 to Module 7. There are recirculation and usage relationships of intermediate products between modules, and carbon footprint allocation is performed according to the quality allocation principle. The independent carbon footprint list for each module, excluding intermediate product recirculation, is continuously input data, with the goal of calculating the cumulative carbon footprint of each module.

[0084] By constructing a system of equations and using the module cumulative carbon footprint model, the relationship between the module's cumulative carbon footprint and its independent carbon footprint can be determined as follows: Convert to the following formula: For any module, Expanding this into a system of n equations with n unknowns, and rearranging it into an n*n matrix: In this embodiment, each module receives an intermediate product from another module, so k is omitted.

[0085] Therefore, the carbon footprint calculation for styrene products is as follows: Establish a quality allocation matrix A, which is 7×7, and the elements of matrix A are A. ji This represents the quality fraction of intermediate products from module j used by module i in module j, and is a negative value, i.e., -fIP. j-i When i = j, Aji =1, indicating the module's own contribution. Then matrix A is defined as: A ji =1, diagonal element; A ji = -(quality fraction of intermediate product from module j used by module i, i≠j).

[0086] Construct an independent carbon footprint matrix B, which is 1×7, and the elements of matrix B are... i Indicates the independent carbon footprint (ICF) of module i. i That is, the sum of the carbon footprints of all other inventory data in module i, excluding the reflux intermediate products, is used as the known input.

[0087] Establish the matrix X to be solved The matrix to be solved, X, is the cumulative carbon footprint matrix X, and the elements of matrix X are X. i Represents the cumulative carbon footprint CF of module i i The goal is to solve for X.

[0088] Constructing matrix equations According to the principle of conservation of matter and carbon footprint, the cumulative carbon footprint of a module satisfies the following system of linear equations: A×X=B.

[0089] The matrix X is solved using matrix operations and conventional linear algebra methods for inverse matrices: X = A -1 ×B, where A -1 The inverse of matrix A is calculated using the following formula:

[0090] As shown below, by substituting the coefficients of styrene matrices A and B into the matrix and solving, the final carbon footprint results for each module are obtained. Solving the matrix yields the result for matrix X, which is the CF... i Calculation results.

[0091]

[0092] S7. Evaluate the inventory data, optimize the data based on the evaluation results, and feed the optimized inventory data back into the carbon footprint model for carbon footprint calculation.

[0093] The system acquires characteristic values ​​from the module emission inventory data that affect the changes in the product's carbon footprint, calculates the material sensitivity coefficient, sets material quality assessment indicators to calculate the material quality assessment coefficient, constructs a four-quadrant diagram based on the material sensitivity coefficient and the quality assessment coefficient, classifies materials into the four-quadrant diagram to determine material data assessment, and optimizes the material data based on the assessment.

[0094] Calculating the material sensitivity coefficient includes: The feature values ​​are obtained, including material values ​​and corresponding material carbon footprint factors. The carbon footprint of the target product is calculated based on the cumulative carbon footprint of the module. The material sensitivity coefficient is obtained by calculating the product of the material value and the material carbon footprint factor, and then comparing it with the carbon footprint of the target product.

[0095] Sensitivity analysis is performed on the values ​​in the styrene lifecycle inventory data to obtain the relative impact of changes in each inventory data point on the carbon footprint calculation results; this is the sensitivity coefficient. A high sensitivity coefficient for a data point indicates a significant impact of changes on the results. The impact of inventory data on carbon footprint results comes from two aspects: changes in the material values ​​themselves, such as material input, transportation distance, and energy consumption; and changes in the corresponding carbon footprint coefficient. Whether changing the material value or the carbon footprint factor, the formula for the sensitivity coefficient's contribution to the total carbon footprint remains the same: Sensitivity Coefficient S. i The calculation formula is expressed as follows: Among them, A i For the i-th material value, EF i Let be the carbon footprint factor of the i-th material, and PCF be the carbon footprint of the target product of the module.

[0096] The calculation of material quality assessment coefficients includes: Set the evaluation benchmark and corresponding score value for each quality assessment indicator, and obtain the score value of the quality assessment indicator by comparing the material data of the bill of materials with the evaluation benchmark. The quality assessment coefficient is obtained by averaging the scores of each evaluation indicator.

[0097] A five-dimensional Pedigree matrix scoring method was adopted, and quality assessment indicators were set, including temporal representativeness, geographical representativeness, technical relevance, data representativeness, and data completeness. Evaluation criteria for each quality assessment indicator were determined, and each criterion was assigned a corresponding score value ranging from 1 to 5 points. Details are shown in Table 1. Table 1 Material data is compared with the evaluation benchmarks of quality assessment indicators to obtain the corresponding quality assessment indicator scores. The quality assessment coefficient is then calculated by combining the scores of each quality assessment indicator. The quality assessment coefficient is: The quality assessment coefficient is the average of the scores of each assessment indicator.

[0098] A four-quadrant diagram is constructed based on the material sensitivity coefficient and quality assessment coefficient. Materials are then categorized into the four-quadrant diagram to determine material data assessment, including: The material sensitivity coefficient and quality assessment coefficient were calculated using normalization. S_norm = Si / Smax Where S_norm is the normalized sensitivity coefficient, ranging from [0,1], representing the contribution of the data sensitivity coefficient to risk; Smax is the maximum sensitivity coefficient; and DQ... norm The normalized quality assessment coefficient, ranging from [0,1], represents the contribution of data quality to risk. (DQI) max The maximum quality assessment coefficient is 5, and the result is DQI. min The minimum quality assessment coefficient is 1.

[0099] A coordinate system is established using the normalized sensitivity coefficient and the normalized quality assessment coefficient. An intermediate value is set to divide the data into four quadrants, and the material data assessment for each quadrant is set.

[0100] The normalized sensitivity coefficient S_norm and the normalized quality evaluation coefficient DQ are used as the basis for the evaluation. norm Using DQ as the vertical and horizontal axes respectively norm =0.65, S_norm=60% to divide and form a four-quadrant diagram, such as Figure 5 As shown, material data evaluation is set for each quadrant.

[0101] Quadrant 1 Q1 (High Sensitivity, High Data Quality): Key control parameters; data stability should be maintained.

[0102] Quadrant 2 Q2 (High Sensitivity, Low Data Quality): High-risk parameter, data quality needs to be improved to a limited extent.

[0103] Third quadrant Q3 (low sensitivity, low data quality): minor parameter, can be considered for optimization or ignored.

[0104] Quadrant 4 (Q4) (Low sensitivity, high data quality): Stable data items, no adjustment required.

[0105] All emission inventory materials are distributed in a four-quadrant diagram based on the normalized sensitivity coefficient and the normalized quality assessment coefficient. The corresponding assessment is obtained according to the quadrant in which the materials are located, and the data is optimized based on the assessment.

[0106] Identify key material data: Data located in Quadrant I (Q1) and Quadrant II (Q2) indicates that the material data has a significant impact on carbon footprint assessment and requires priority attention. For example, steam and electricity should be given priority.

[0107] Optimize data quality: Located in the second quadrant Q2, this indicates high material sensitivity but low data quality, requiring priority improvement in data collection and quality control. For example, steam, with its high sensitivity and low data quality, should be the primary material for which data quality improvement should be prioritized.

[0108] Reduce computational complexity: If the material data is located in the third quadrant Q3 and the fourth quadrant Q4, it means that the material data has little impact on the carbon footprint results. Under the premise of meeting the rounding rules, we can consider reducing or ignoring the material data in subsequent calculations.

[0109] To quickly identify high-sensitivity material data with excessively low quality that could affect model quality, a scoring lower limit judgment mechanism is set, as shown in Table 2. Table 2 <![CDATA[S i ]]> DQ limit <![CDATA[DQ norm Limits More than 20% 1.6 0.85 15～20％ 2 0.75 10％～15％ 2.4 0.65 5％～10％ 3.2 0.45 Below 5% 3.8 0.3 If the material data is normalized to a quality assessment coefficient DQ norm If the data falls below the range limit, it is marked as exceeding the risk limit. Material data points exceeding the limit mechanism are marked with "X" in the four-quadrant diagram, indicating that the sensitivity and quality of the material exceed the reasonable range. Other data points in the four-quadrant diagram are marked with "O", indicating that the material data is within the reasonable range.

[0110] Risk Control: By viewing the points marked with "X", material data with sensitivity and quality exceeding reasonable ranges can be quickly identified for further review or optimization. For example, C8 aromatics, although low in sensitivity, have poor data quality and exceed limits; therefore, data quality work should be given higher priority.

[0111] The optimized data is fed back into the carbon footprint model to recalculate the cumulative carbon footprint, making the carbon footprint data more accurate.

[0112] This embodiment also includes a carbon footprint accounting system for styrene products optimized with inventory data, used to implement the above method, including: The target determination unit identifies the target products and the scope of accounting. The module deconstruction unit deconstructs the target product's process flow into multiple modules; The model generation unit establishes a module-independent carbon footprint model and, based on the input intermediate product carbon footprint and the module-independent carbon footprint model, establishes a module-cumulative carbon footprint model. The carbon footprint calculation unit calculates the cumulative carbon footprint of the module by combining the cumulative carbon footprint models of all modules. The data optimization unit calculates the material sensitivity coefficient and material quality assessment coefficient, constructs a four-quadrant diagram, classifies materials into the four-quadrant diagram to determine material data assessment, and optimizes the material data based on the assessment.

[0113] The specific embodiments described herein are merely illustrative of the spirit of the invention. Those skilled in the art to which this invention pertains may make various modifications or additions to the described specific embodiments or use similar methods to substitute them, without departing from the spirit of the invention or exceeding the scope defined by the appended claims.

Claims

1. A method for calculating the carbon footprint of styrene products based on inventory data optimization, characterized in that, Includes the following steps: Modular deconstruction of the target product's process flow; Constructing an emissions inventory and collecting emissions inventory data; The carbon footprint of the module products is allocated by quality, and the cumulative carbon footprint of intermediate products is added to construct the module's cumulative carbon footprint model. Calculate the cumulative carbon footprint of the module by combining the cumulative carbon footprint models of all modules; The system acquires characteristic values ​​from the module emission inventory data that affect the changes in the product's carbon footprint, calculates the material sensitivity coefficient, sets material quality assessment indicators to calculate the material quality assessment coefficient, constructs a four-quadrant diagram based on the material sensitivity coefficient and the quality assessment coefficient, classifies materials into the four-quadrant diagram to determine material data assessment, and optimizes the material data based on the assessment.

2. The carbon footprint accounting method for styrene products based on inventory data optimization according to claim 1, characterized in that, Calculating the material sensitivity coefficient includes: The feature values ​​are obtained, including material values ​​and corresponding material carbon footprint factors. The carbon footprint of the target product is calculated based on the cumulative carbon footprint of the module. The material sensitivity coefficient is obtained by calculating the product of the material value and the material carbon footprint factor, and then comparing it with the carbon footprint of the target product.

3. The carbon footprint accounting method for styrene products based on inventory data optimization according to claim 1, characterized in that, The calculation of material quality assessment coefficients includes: Set the evaluation benchmark and corresponding score value for each quality assessment indicator, and obtain the score value of the quality assessment indicator by comparing the material data of the bill of materials with the evaluation benchmark. The quality assessment coefficient is obtained by averaging the scores of each evaluation indicator.

4. A method for calculating the carbon footprint of styrene products based on inventory data optimization according to claim 1, 2, or 3, characterized in that, A four-quadrant diagram is constructed based on the material sensitivity coefficient and quality assessment coefficient. Materials are then categorized into the four-quadrant diagram to determine material data assessment, including: The material sensitivity coefficient and quality assessment coefficient were calculated using normalization. A coordinate system is established using the normalized sensitivity coefficient and the normalized quality assessment coefficient. An intermediate value is set to divide the data into four quadrants, and the material data assessment for each quadrant is set. Materials are categorized into a four-quadrant diagram based on normalized sensitivity coefficients and normalized quality assessment coefficients to obtain material data evaluation.

5. The carbon footprint accounting method for styrene products based on inventory data optimization according to claim 1, characterized in that, The modular deconstruction of the target product's process flow includes: Define the target product's process flow, and divide the process flow into modules, with the production unit where the product flows out and the data is available as the smallest unit, resulting in multiple modules.

6. The carbon footprint accounting method for styrene products based on inventory data optimization according to claim 2, characterized in that, Assigning module product carbon footprint by quality includes: Obtain the total mass of all output products of the module, including target products, intermediate products and by-products. Calculate the proportion of each product in the total mass of all products to obtain the mass score of each product. Calculate the carbon footprint of each product based on the module's cumulative carbon footprint and the product's mass score.

7. The carbon footprint accounting method for styrene products based on inventory data optimization according to claim 6, characterized in that, The cumulative carbon footprint model, which builds upon the carbon footprint of intermediate products, includes: Establish a module-independent carbon footprint model based on the module's inputs and outputs; Obtain the carbon footprint of the intermediate products from the input module; A module cumulative carbon footprint model is established by combining the input intermediate product carbon footprint and the module independent carbon footprint model.

8. The carbon footprint accounting method for styrene products based on inventory data optimization according to claim 7, characterized in that, The cumulative carbon footprint of the module is calculated by combining the cumulative carbon footprint models of all modules, including: Obtain the cumulative carbon footprint model construction equation set for all modules; Establish a quality allocation matrix A based on the system of equations. The elements of matrix A are the quality fractions of intermediate products from module j used by module i in module j. Establish a module-independent carbon footprint matrix B, where the elements of matrix B are the independent carbon footprints of each module; Establish a matrix X to be solved, where the elements of matrix X are the cumulative carbon footprints of each module; Construct matrix equations and solve matrix X to obtain the cumulative carbon footprint of each module.

9. A method for calculating the carbon footprint of styrene products based on inventory data optimization according to claim 7 or 8, characterized in that, Based on the module inputs and outputs, establish a module-independent carbon footprint model, including: Determine the scope of accounting for the target product; Based on the module deconstruction results, the flow relationships between modules are clarified, the emission information within each module is integrated, and a module manifold diagram is constructed. Establish an emissions inventory and obtain emissions inventory data; Establish modular independent carbon footprint models based on direct emissions, indirect energy emissions, and emissions from upstream and downstream of the supply chain.

10. A carbon footprint accounting system for styrene products optimized by inventory data, implementing the method according to any one of claims 1-9, characterized in that: The target determination unit identifies the target products and the scope of accounting. The module deconstruction unit deconstructs the target product's process flow into multiple modules; The model generation unit establishes a module-independent carbon footprint model and a module-cumulative carbon footprint model based on the input intermediate product carbon footprint and the independent carbon footprint model. The carbon footprint calculation unit calculates the cumulative carbon footprint of the module by combining the cumulative carbon footprint models of all modules. The data optimization unit calculates the material sensitivity coefficient and material quality assessment coefficient, constructs a four-quadrant diagram, classifies materials into the four-quadrant diagram to determine material data assessment, and optimizes the material data based on the assessment.

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